Sibyl

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: it performs web searches, reads multiple sources, and synthesizes reports with optional predictions. However, it doesn't mention important aspects like rate limits, authentication needs, execution time, or error handling. The depth parameter explanation adds some behavioral context about query counts.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is perfectly structured and concise: a clear purpose statement, followed by bullet-like explanation of the process, then detailed parameter documentation. Every sentence earns its place by adding essential information. No wasted words or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (multi-step research process), no annotations, and an output schema exists (so return values needn't be explained), the description is quite complete. It covers purpose, process, and parameters well. However, for a tool with no annotations, it could benefit from mentioning execution characteristics like time requirements or limitations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must fully compensate. It provides excellent parameter semantics: query is explained with an example, depth has detailed levels with specific query counts, and language lists options with 'auto' behavior. This adds substantial meaning beyond the bare schema with no descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('Run a deep research cycle', 'Searches the web', 'reads multiple sources', 'synthesizes a comprehensive report') and distinguishes it from siblings like quick_search (which likely does shallow searches) and read_url (which reads single sources). It specifies the output format (report with findings, analysis, predictions).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool ('Run a deep research cycle on any topic') and implies depth levels, but doesn't explicitly state when to choose alternatives like quick_search (for shallow research) or read_url (for single-source reading). It distinguishes from siblings by emphasizing comprehensive synthesis, but lacks explicit 'when-not' guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.